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2023 | Book

Enterprise Risk Management in Supply Chains Read chapter Read first chapter

Enterprise Risk Management Models

Focus on Sustainability

Authors: David L. Olson, Desheng Wu

Publisher: Springer Berlin Heidelberg

Book Series : Springer Texts in Business and Economics

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This textbook, now in its fourth edition, serves as a comprehensive guide to learning various aspects of risk, encompassing supply chain management, artificial intelligence, and sustainability. It demonstrates a wide range of operations research models that have been successfully applied to enterprise supply chain risk management. Each chapter of the book can function as a standalone module focusing on a specific topic, offering dedicated examples, definitions, and discussion notes.

The publication of this book comes at a crucial time when the world is facing increasing challenges from various forms of risk. Events such as Covid-19, the energy crisis, wars, and terrorism in the 21st century have all disrupted supply chains, thus highlighting the critical importance of enterprise risk management. Additional risks, such as financial and technological bubbles, along with concerns surrounding rampant artificial intelligence, contribute to a climate that demands enhanced risk management within organizations.

Table of Contents

Frontmatter
1. Enterprise Risk Management in Supply Chains
Abstract
This chapter introduces risk management by reviewing some major risk events faced by businesses in the past. Unexpected consequences of human activity are discussed, with a presentation of the accounting organizational risk management framework. This approach is compared to traditional risk management. Supply chain risk management frameworks and mitigation strategies are given.
David L. Olson, Desheng Wu
2. Risk Matrices
Abstract
Risk matrices are simplified frameworks for implementation of risk management policies in a given domain. This chapter describes risk matrices and gives guidance for their construction. Examples in business contexts are provided.
David L. Olson, Desheng Wu
3. Value-Focused Supply Chain Risk Analysis
Abstarct
Business decisions usually involve multiple conflicting objectives. This chapter reviews processes to structure such decisions, and provides a detailed method of the simple multi-attribute rating theory (SMART) to analyze such decisions in a systematic manner. Calculations are demonstrated.
David L. Olson, Desheng Wu
4. Examples of Supply Chain Decisions Trading-off Criteria
Abstarct
While Chapter 3 reviewed the SMART method, this chapter provides five cases where multi-attribute analysis was applied in supply chain environments. Each decision case is explained, and computations demonstrated using the SMART method.
David L. Olson, Desheng Wu
5. Simulation of Supply Chain Risk
Abstract
Many supply chain problem analyses involve uncertainty in the form of statistically measured distributions. Monte Carlo simulation is a highly useful tool to analyze statistical distributions and to model many supply chain decisions involving risk. Inventory management and vendor selection decisions are demonstrated using Crystal Ball software.
David L. Olson, Desheng Wu
6. Value at Risk Models
Abstarct
Financial risk management has been analyzed using the value at risk methodology. The method is described, evolving from variance-covariance analysis. Monte Carlo simulation is applied to demonstrate calculations with Crystal Ball software.
David L. Olson, Desheng Wu
7. Chance-Constrained Models
Abstract
Linear programming is a traditional means of optimizing many supply chain decisions. However, linear programming assumes linear relationships, yielding extreme solutions that become risky. Chance constraints are a way to incorporate variance-covariance measures into such optimi9zation models, which this chapter describes and demonstrates.
David L. Olson, Desheng Wu
8. Data Envelopment Analysis in Enterprise Risk Management
Abstract
Data envelopment analysis is a method to assess economic efficiency of decision-making units. The concept is explained and demonstrated with business-related data.
David L. Olson, Desheng Wu
9. Data Mining Models and Enterprise Risk Management
Abstract
Data mining is the application of statistical and artificial intelligence models to decisions and is used in a wide variety of fields. This chapter describes the Rattle open-source software for data mining, applied to a financial risk management decision. Classification models are demonstrated.
David L. Olson, Desheng Wu
10. Balanced Scorecards to Measure Enterprise Risk Performance
Abstarct
Balanced scorecards are tools to monitor the relative performance of organizational units over multiple dimensions. Traditionally, the four dimensions of learning and growth, internal business processes, customer satisfaction, and financial performance have been used. The concept is demonstrated with multiple business contexts.
David L. Olson, Desheng Wu
11. Machine Learning and Artificial Intelligence Risk
Abstract
The development of artificial intelligence is reviewed. It includes a variety of computer-driven methods. Those applying to risk management data mining applications are reviewed, to include a number of classification models. The expected impact of AI/machine learning on future human activity is discussed.
David L. Olson, Desheng Wu
12. Enterprise Risk Management in Projects
Abstract
Project management is a major activity in many domains. They involve many risks due to their being one-time events. Risk management tools are presented and demonstrated.
David L. Olson, Desheng Wu
13. Natural Disaster Risk Management
Abstarct
Nature is a complex system with many powerful physical events caus8ing disruption of human activities. Emergency management systems are described and modeling options demonstrated.
David L. Olson, Desheng Wu
14. Sustainability and Enterprise Risk Management
Abstract
In addition to natural disasters, there is a systemic continuous challenge to natural environments from developments such as global warming, and from epidemic health risks such as COVID-19. The United Nations view of sustainable development goals is described. Data mining and multi-criteria modeling of sustainability goals are presented.
David L. Olson, Desheng Wu
15. Environmental Damage and Risk Assessment
Abstarct
Methods to assess and evaluate environmental damage are presented, to include cost-benefit analysis, contingent evaluation, conjoint analysis, and habitat equivalency analysis. Methods are demonstrated with risk management contexts.
David L. Olson, Desheng Wu
Backmatter
Metadata
Title
Enterprise Risk Management Models
Authors
David L. Olson
Desheng Wu
Copyright Year
2023
Publisher
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-68038-4
Print ISBN
978-3-662-68037-7
DOI
https://doi.org/10.1007/978-3-662-68038-4